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. 2018 May 18;360(6390):758-763.
doi: 10.1126/science.aar2131. Epub 2018 Apr 5.

Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease

Jihwan Park  1 , Rojesh Shrestha  1 , Chengxiang Qiu  1 , Ayano Kondo  1 , Shizheng Huang  1 , Max Werth  2 , Mingyao Li  3 , Jonathan Barasch  2 , Katalin Suszták  4
Affiliations

Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease

Jihwan Park et al. Science. .

Abstract

Our understanding of kidney disease pathogenesis is limited by an incomplete molecular characterization of the cell types responsible for the organ's multiple homeostatic functions. To help fill this knowledge gap, we characterized 57,979 cells from healthy mouse kidneys by using unbiased single-cell RNA sequencing. On the basis of gene expression patterns, we infer that inherited kidney diseases that arise from distinct genetic mutations but share the same phenotypic manifestation originate from the same differentiated cell type. We also found that the collecting duct in kidneys of adult mice generates a spectrum of cell types through a newly identified transitional cell. Computational cell trajectory analysis and in vivo lineage tracing revealed that intercalated cells and principal cells undergo transitions mediated by the Notch signaling pathway. In mouse and human kidney disease, these transitions were shifted toward a principal cell fate and were associated with metabolic acidosis.

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Figures

Fig. 1.
Fig. 1.. Cell diversity in mouse kidney cells delineated by single cell transcriptomic analysis.
(A) Unsupervised clustering demonstrates 16 distinct cell types shown in a tSNE map. Left panels are subclusters of clusters 1, 3, and 7. Percentage of assigned cell types are summarized in the right panel. Endo: containing endothelial, vascular, and descending loop of Henle, Podo: podocyte, PT: proximal tubule, LOH: ascending loop of Henle, DCT: distal convoluted tubule, CD-PC: collecting duct principal cell, CD-IC: CD intercalated cell, CD-Trans: CD transitional cell, Fib: fibroblast, Macro: macrophage, Neutro: neutrophil, NK: natural killer cell. (B and C) Violin plots showing the expression level of representative marker genes across the 16 main clusters. Y-axis is log scale normalized read count. (C) Cluster1 (from panel A left) separates into endothelial cells, pericytes/vascular smooth muscle cells and descending loop of Henle (DLH). Cluster3 (proximal tubules) separates into S1, S2 and S3 segments or PCT (proximal convoluted tubules) and PST (proximal straight tubules). Cluster 7 intercalated cells separates into A and B type intercalated cells.
Fig. 2.
Fig. 2.. Discrete human disease phenotypes are due to mutations in single specific cell types
Single cell-type specific average expression of human (A) monogenic disease genes and (B) complex trait genes identified by GWAS. Mean expression values of the genes were calculated in each cluster. The color scheme is based on z-score distribution; the map shows genes with z-score>2. In the heatmap, each row represents one gene and each column is single cell type (defined in Fig. 1). The full list of cell types and genes are shown in fig. S11 and 12.
Fig. 3.
Fig. 3.. Identification of a transitional cell type and a conversion process in the collecting duct.
(A) The expression level of marker genes across the 16 clusters. Y-axis is log scale normalized read count. (B) Gene expression levels in PC (Aquaporin 2, Aqp2), IC (H+ATPase, Atp6v1g3,) and in Transitional cells (Syt7) demonstrated by a tSNE plot. (C) Representative immunofluorescence images of AQP2 (PC marker), ATP6V1B1 (IC marker) and DAPI in the kidney collecting duct. Arrow demonstrates transitory PC/IC cell type expressing AQP2 and ATP6V1B1. (D) Heatmap showing the expression level of differentially expressed genes in collecting duct cell types. Color scheme is based on z-score distribution. (E) Venn diagram showing the overlaps of differentially expressed genes between PC, IC and the novel cell type. (F) Immunofluorescence staining for PARM1 (IC specific) and AQP2 (upper panel) or ATP6V1B1 (lower panel) in the kidney collecting duct. A “double positive” cell is shown at the arrow (G) Ordering single cells along a cell conversion trajectory using Monocle. Three collecting duct cell clusters were used for ordering and plotted on low dimensional space with different colors. The tSNE plots next to the trajectory map show differentially expressed genes in the corresponding cell lineages. (H) Aqp2CremT/mG mouse model used for lineage tracing of AQP2 positive cells. Immunofluorescence staining for GFP, ATP6V1B1 and AQP2. Right panel is the quantification of GFP positive cells (mean ± SD). n=3. Note that AQP2 driven GFP (white) is found in PC (red + white) and IC (green + white) and in transitional cells (not shown) (I) Atp6CremT/mG mouse model used for lineage tracing of ATP6ase positive cells. Immunofluorescence staining for GFP, ATP6V1B1 and AQP2 in Atp6CremT/mG mice. Note that ATP6V1B1 driven GFP (white) is found in PC (red+ white) and IC (green+ white) and in transitional cells (red+ green+ white).
Fig. 4.
Fig. 4.. The IC-to- PC cell transition is driven by Notch ligand and receptor expression.
(A) Transcriptional profiles demonstrating the spectrum of expression of Notch genes in the collecting duct. Cells are ordered in pseudotime and color represents expression levels. (B) Double immunofluorescence staining for AQP2 (red) and JAG1 (green) in the kidney collecting duct. (C) Generation of mice with inducible expression of Notch (ICN1) in kidney tubules. Excess AQP2+ cells and conversely reciprocally decreased ATP6V1B1+ positive cells are found in Pax8rtTA/NICD tubules (mean ± SD). n=3. Asterisk represents significant difference; p-value < 0.01. (D) In silico deconvolution of mouse kidney bulk RNA profiling data. Wild type (WT) and Pax8rtTA/NICD samples were used for analysis. (E) Immunofluorescence quantification of cells labeled with AQP2 and ATP6V1B1 in control and FA mice (mean ± SD). n=3. Asterisk represents significant differences; p-value < 0.01. (F) In silico deconvolution of mouse kidney bulk RNA profiling. Control and kidney samples from FA injected mice were used for analysis. (G) Immunofluorescence staining for AQP2 and ATP6V1B1 in control, Pax8rtTA/NICD and FA collecting ducts. Note the abundance of AQP2+ cells in Pax8rtTA/NICD and FA and conversely the disappearance of ATP6V1B1+ cells (H) In silico deconvolution of bulk RNA profiling data from normal and CKD human samples (n=91). The histological fibrosis scores and HES1 expression levels for the corresponding samples are also shown. (I) Total serum bicarbonate level in control and FA induced kidney fibrosis (mean ± SD). n=5.
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